Aesthetics analysis with deep learning
Aesthetics of images has a strong subjective element and possibility of multiple dimensions depending on purpose of the media. That means:


It is hard to define what we mean by scoring aesthetics.
Given any well-constrained definition, it is then time-consuming to collect relevant data.


However, there is some interest in the machine-learning community, as media quality would be a very useful metric to sort and filter data on (provided the metric is close enough to the end user who wants to select it). As a result, there are data sets, research papers and pre-built models for this.

Media quality training data can be crowdsourced in a variety of ways, including looking at popularity of items on social media, to paying experts to assess large numbers of images. An example of one open dataset compiled by researchers for this purpose is called AVA.

This data might be reduced to image/quality pairs which you can then train a CNN model to predict the quality metric (score out of 10 for example). This might just be a regression, but other more complex loss functions are also considered. 

A quick search for existing models brings up Google's NIMA project, which has more than one implementation available as open-source code. NIMA appears to use multiclass classification approach to predict which ratings humans would most likely give the image, and the resulting score is then a weighted average of the predicted scores - the claimed benefit of that seems to be that it better matches how the quality ratings are sourced, and it will better capture split opinions (e.g. half of people think the image is terrible, but half think it is great is a different type of image to one where everyone thinks it is just average). 

Here is an implementation of NIMA by Github account "idealo" looks complete with documentation, and ready to use with pre-built scripts.

Just to show this is not a one-off, here's a blog by Andrej Karpathy about using CNNs to rate selfies which includes some introduction to core CNN concepts.